Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2933-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2933-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
27 Jun 2025
Data description article |
| 27 Jun 2025
| 27 Jun 2025
An annual 30 m cultivated-pasture dataset of the Tibetan Plateau from 1988 to 2021
Binghong Han
State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, and College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
College of Earth and Environmental Sciences, and Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China
Shengli Tao
Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
Tong Yang
College of Earth and Environmental Sciences, and Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China
Yongli Tang
College of Earth and Environmental Sciences, and Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China
Mengshuai Ge
Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
State Key Laboratory of Seed Innovation and Grassland Agro-ecosystems, and College of Ecology, Lanzhou University, Lanzhou 730000, China
Zhenong Jin
Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
Jinwei Dong
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Zhibiao Nan
State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, and College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, and College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
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Cited articles
Aghighi, H., Azadbakht, M., Ashourloo, D., Shahrabi, H. S., and Radiom, S.: Machine learning regression techniques for the silage maize yield prediction using time-series images of Landsat 8 OLI, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 11, 4563–4577, https://doi.org/10.1109/JSTARS.2018.2823361, 2018.
Ashourloo, D., Shahrabi, H. S., Azadbakht, M., Aghighi, H., Matkan, A. A., and Radiom, S.: A novel automatic method for alfalfa mapping using time series of Landsat-8 OLI data, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 11, 4478–4487, https://doi.org/10.1109/jstars.2018.2874726, 2018.
Bardgett, R. D., Bullock, J. M., Lavorel, S., Manning, P., Schaffner, U., Ostle, N., Chomel, M., Durigan, G., Fry, E. L., Johnson, D., Lavallee, J. M., Provost, G. L., Luo, S., Png, K., Sankaran, M., Hou, X., Zhou, H., Ma, L., Ren, W., Li, X., Ding, Y., Li, Y., and Shi, H.: Combatting global grassland degradation, Nat. Rev. Earth Environ., 2, 720–735, https://doi.org/10.1038/s43017-021-00207-2, 2021.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Chang, J., Ciais, P., Gasser, T., Smith, P., Herrero, M., Havlík, P., Obersteiner, M., Guenet, B., Goll, D. S., Li, W., Naipal, V., Peng, S., Qiu, C., Tian, H., Viovy, N., Yue, C., and Zhu, D.: Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands, Nat. Commun., 12, 118, https://doi.org/10.1038/s41467-020-20406-7, 2021.
Chen, H., Ju, P., Zhu, Q., Xu, X., Wu, N., Gao, Y., Feng, X., Tian, J., Niu, S., Zhang, Y., Peng, C., and Wang, Y.: Carbon and nitrogen cycling on the Qinghai–Tibetan Plateau, Nat. Rev. Earth Environ., 3, 701–716, https://doi.org/10.1038/s43017-022-00344-2, 2022.
Chen, L., Jiang, L., Jing, X., Wang, J., Shi, Y., Chu, H., and He, J.-S.: Above and belowground biodiversity jointly drive ecosystem stability in natural alpine grasslands on the Tibetan Plateau, Global Ecol. Biogeogr., 30, 1418–1429, https://doi.org/10.1111/geb.13307, 2021.
Cheng, Y., Cai, Y., and Wang, S.-Q.: Yak and Tibetan sheep dung return enhance soil N supply and retention in two alpine grasslands in the Qinghai-Tibetan Plateau, Biol. Fertil., 52, 413–422, https://doi.org/10.1007/s00374-016-1088-6, 2016.
Ding, L., Kapp, P., Cai, F., Garzione, C. N., Xiong, Z., Wang, H., and Wang, C.: Timing and mechanisms of Tibetan Plateau uplift, Nat. Rev. Earth Environ., 3, 652–667, https://doi.org/10.1038/s43017-022-00318-4, 2022.
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., and Moore, B.: Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine, Remote Sens. Environ., 185, 142–154, https://doi.org/10.1016/j.rse.2016.02.016, 2016.
Dong, S., Shang, Z., Gao, J., and Boone, R.: Enhancing the ecological services of the Qinghai-Tibetan Plateau's grasslands through sustainable restoration and management in era of global change, Agr. Ecosyst. Environ., 326, 107756, https://doi.org/10.1016/j.agee.2021.107756, 2022.
Fang, J., Bai, Y., Li, L., Jiang, G., Huang, J., Huang, Z., Zhang, W., and Gao, S.: Scientific basis and practical ways for sustainable development of China's pasture regions, Chin. Sci. Bull., 61, 155–164, https://doi.org/10.1360/N972015-00718, 2016.
Farr, T., Rosen, P., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005rg000183, 2007.
Fisher, R. J., Sawa, B., and Prieto, B.: A novel technique using LiDAR to identify native-dominated and tame-dominated grasslands in Canada, Remote Sens. Environ., 218, 201–206, https://doi.org/10.1016/j.rse.2018.10.003, 2018.
Foody, G. M.: Status of land cover classification accuracy assessment, Remote Sens. Environ., 80, 185–201, https://doi.org/10.1016/s0034-4257(01)00295-4, 2002.
Friedl, M. A., Woodcock, C., Gopal, S., Muchoney, D., Strahler, A. H., and Barker-Schaaf, C.: A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data, Int. J. Remote Sens., 21, 1073–1077, https://doi.org/10.1080/014311600210434, 2000.
Fuglie, K., Peters, M., and Burkart, S.: The extent and economic significance of cultivated forage crops in developing countries, Front. Sustain. Food Syst., 5, 712136, https://doi.org/10.3389/fsufs.2021.712136, 2021.
Gao, B.: NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257–266, https://doi.org/10.1016/s0034-4257(96)00067-3, 1996.
García, M. J. L. and Caselles, V.: Mapping burns and natural reforestation using thematic mapper data, Geocarto Int., 6, 31–37, https://doi.org/10.1080/10106049109354290, 1991.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Han, B., Bi, J., Tao, S., Yang, T., Tang, Y., Ge, M., Wang, H., Jin, Z., Dong, J., Nan, Z., and He, J.-S.: An annual 30 m cultivated pasture dataset of the Tibetan Plateau from 1988 to 2021, Zenodo [data set], https://doi.org/10.5281/zenodo.14271782, 2024.
He, C., Braun, J., Tang, H., Yuan, X., Acevedo-Trejos, E., Ott, R. F., and de Quay, G. S.: Drainage divide migration and implications for climate and biodiversity, Nat. Rev. Earth Environ., 5, 1–16, https://doi.org/10.1038/s43017-023-00511-z, 2024.
He, J.-S., Dong, S., Shang, Z., Sundqvist, M., Wu, G., and Yang, Y.: Above-belowground interactions in alpine ecosystems on the roof of the world, Plant Soil., 458, 1–6, https://doi.org/10.1007/s11104-020-04761-4, 2020.
Hou, L., Xia, F., Chen, Q., Huang, J., He, Y., Rose, N., and Rozelle, S.: Grassland ecological compensation policy in China improves grassland quality and increases herders' income, Nat. Commun., 12, 4683, https://doi.org/10.1038/s41467-021-24942-8, 2021.
Jia, J., Cao, Z., Liu, C., Zhang, Z., Lin, L., Wang, Y., Haghipour, N., Wacker, L., Bao, H., Dittmar, T., Simpson, M. J., Yang, H., Crowther, T. W., Eglinton, T. I., He, J.-S., and Feng, X.: Climate warming alters subsoil but not topsoil carbon dynamics in alpine grassland, Global Change Biol., 25, 4383–4393, https://doi.org/10.1111/gcb.14823, 2019.
Kumar, S., Singh, A. K., Singh, R., Ghosh, A., Chaudhary, M., Shukla, A. K., Kumar, S., Singh, H. V., Ahmed, A., and Kumar, R. V.: Degraded land restoration ecological way through horti-pasture systems and soil moisture conservation to sustain productive economic viability, Land Degrad. Dev., 30, 1516–1529, https://doi.org/10.1002/ldr.3340, 2019.
Li, C., Fu, B., Wang, S., Stringer, L. C., Wang, Y., Li, Z., Liu, Y., and Zhou, W.: Drivers and impacts of changes in China's drylands, Nat. Rev. Earth Environ., 2, 858–873, https://doi.org/10.1038/s43017-021-00226-z, 2021.
Li, X. and Wang, J. (Eds.): China Pratacultural Statistics, China Agricultural Press, Beijing, ISBN 978-7-109-29243-7, 2017.
Li, Y., Degen, A. A., Sun, T., Wang, W., Bai, Y., Zhang, T., Long, R., and Shang, Z.: Three years of cultivating or fencing lands have different impacts on soil nutrients and properties of a subalpine meadow in the Tibetan plateau, Catena, 186, 104306, https://doi.org/10.1016/j.catena.2019.104306, 2020.
Liu, H. Q. and Huete, A.: A feedback based modification of the NDVI to minimize canopy background and atmospheric noise, IEEE T. Geosci. Remote, 33, 457–465, https://doi.org/10.1109/tgrs.1995.8746027, 1995.
López García, M. J. and Caselles, V.: Mapping burns and natural reforestation using thematic mapper data, Geocarto Int., 6, 31–37, https://doi.org/10.1080/10106049109354290, 1991.
McInnes, W. S., Smith, B., and McDermid, G. J.: Discriminating native and non-native grasses in the dry mixedgrass prairie with MODIS NDVI time series, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 8, 1395–1403, https://doi.org/10.1109/jstars.2015.2416713, 2015.
Moon, M., Richardson, A. D., and Friedl, M. A.: Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery, Remote Sens. Environ., 266, 112716, https://doi.org/10.1016/j.rse.2021.112716, 2021.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A.: Good practices for estimating area and assessing accuracy of land change, Remote Sens. Environ., 148, 42–57, https://doi.org/10.1016/j.rse.2014.02.015, 2014.
Parente, L. and Ferreira, L.: Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016, Remote Sens., 10, 606, https://doi.org/10.3390/rs10040606, 2018.
Parente, L., Ferreira, L., Faria, A., Nogueira, S., Araújo, F., Teixeira, L., and Hagen, S.: Monitoring the brazilian pasturelands: A new mapping approach based on the landsat 8 spectral and temporal domains, Int. J. Appl. Earth Obs. Geoinf., 62, 135–143, https://doi.org/10.1016/j.jag.2017.06.003, 2017.
Parente, L., Mesquita, V., Miziara, F., Baumann, L., and Ferreira, L.: Assessing the pasturelands and livestock dynamics in Brazil, from 1985 to 2017: A novel approach based on high spatial resolution imagery and Google Earth Engine cloud computing, Remote Sens. Environ., 232, 111301, https://doi.org/10.1016/j.rse.2019.111301, 2019.
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., and Ranagalage, M.: Sentinel-2 Data for Land Cover/Use Mapping: A Review, Remote Sens., 12, 2291, https://doi.org/10.3390/rs12142291, 2020.
Piao, S., Wang, X., Park, T., Chen, C., Lian, X., He, Y., Bjerke, J. W., Chen, A., Ciais, P., Tømmervik, H., Nemani, R. R., and Myneni, R. B.: Characteristics, drivers and feedbacks of global greening, Nat. Rev. Earth Environ., 1, 14–27, https://doi.org/10.1038/s43017-019-0001-x, 2020.
Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., and Cortez, J.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century, Nat. Food., 3, 19–28, https://doi.org/10.1038/s43016-021-00429-z, 2022.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward, A. S., Bindschadler, R., Cohen, W. B., Gao, F., Hipple, J. D., Hostert, P., Huntington, J., Justice, C. O., Kilic, A., Kovalskyy, V., Lee, Z. P., Lymburner, L., Masek, J. G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R. H., and Zhu, Z.: Landsat-8: Science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154–172, https://doi.org/10.1016/j.rse.2014.02.001, 2014.
Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., and Egorov, A.: Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sens. Environ., 185, 57–70, https://doi.org/10.1016/j.rse.2015.12.024, 2016.
Schils, R. L., Bufe, C., Rhymer, C. M., Francksen, R. M., Klaus, V. H., Abdalla, M., Milazzo, F., Lellei-Kovács, E., ten Berge, H., and Bertora, C.: Permanent grasslands in Europe: Land use change and intensification decrease their multifunctionality, Agr. Ecosyst. Environ., 330, 107891, https://doi.org/10.1016/j.agee.2022.107891, 2022.
Stehman, S. V. and Foody, G. M.: Key issues in rigorous accuracy assessment of land cover products, Remote Sens. Environ., 231, 111199, https://doi.org/10.1016/j.rse.2019.05.018, 2019.
Tian, J., Zhang, Z., Philpot, W. D., Tian, Q., Zhan, W., Xi, Y., Wang, X., and Zhu, C.: Simultaneous estimation of fractional cover of photosynthetic and non-photosynthetic vegetation using visible-near infrared satellite imagery, Remote Sens. Environ., 290, 113549, https://doi.org/10.1016/j.rse.2023.113549, 2023.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
Vermote, E., Roger, J.-C., Franch, B., and Skakun, S.: LaSRC (Land Surface Reflectance Code): Overview, application and validation using MODIS, VIIRS, LANDSAT and Sentinel 2 data's, in: IEEE Geosci. Remote Sens. Symp., 22–27 July 2018, Valencia, Spain, 8173–8176, https://doi.org/10.1109/IGARSS.2018.8517622, 2018.
Vroey, M. D., de Vendictis, L., Zavagli, M., Bontemps, S., Heymans, D., Radoux, J., Koetz, B., and Defourny, P.: Mowing detection using Sentinel-1 and Sentinel-2 time series for large scale grassland monitoring, Remote Sens. Environ., 280, 113145, https://doi.org/10.1016/j.rse.2022.113145, 2022.
Wang, C., Chen, J., Wu, J., Tang, Y., Shi, P., Black, T. A., and Zhu, K.: A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems, Remote Sens. Environ., 196, 1–12, https://doi.org/10.1016/j.rse.2017.04.031, 2017.
Wang, H., Liu, H., Cao, G., Ma, Z., Li, Y., Zhang, F., Zhao, X., Zhao, X., Jiang, L., Sanders, N., Classen, A., and He, J.-S.: Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change, Ecol. Lett., 23, 701–710, https://doi.org/10.1111/ele.13474, 2020.
Wang, P., Wang, J., Elberling, B., Yang, L., Chen, W., Song, L., Yan, Y., Wang, S., Pan, J., and Niu, S.: Increased annual methane uptake driven by warmer winters in an alpine meadow, Global Change Biol., 28, 3246–3259, https://doi.org/10.1111/gcb.16120, 2022a.
Wang, R., Feng, Q., Jin, Z., Ma, K., Zhang, Z., and Liang, T.: Identification and area information extraction of oat pasture based on GEE – A case study in the Shandan Racecourse (China), Remote Sens., 14, 4358, https://doi.org/10.3390/rs14174358, 2022b.
Wang, T. and Zhang, W.-H.: Priorities for the development of alfalfa pasture in northern China, Fundament. Res., 3, 225–228, https://doi.org/10.1016/j.fmre.2022.04.017, 2023.
Wang, Y., Lv, W., Xue, K., Wang, S., Zhang, L., Hu, R., Zeng, H., Xu, X., Li, Y., Jiang, L., Hao, Y., Du, J., Sun, J., Dorji, T., Piao, S., Wang, C., Luo, C., Zhang, Z., Chang, X., Zhang, M., Hu, Y., Wu, T., Wang, J., Li, B., Liu, P., Zhou, Y., Wang, A., Dong, S., Zhang, X., Gao, Q., Zhou, H., Shen, M., Wilkes, A., Miehe, G., Zhao, X., and Niu, H.: Grassland changes and adaptive management on the Qinghai–Tibetan Plateau, Nat. Rev. Earth Environ., 3, 668–683, https://doi.org/10.1038/s43017-022-00330-8, 2022c.
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., Allen, R. G., Anderson, M. C., Belward, A. S., Cohen, W. B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J. D., Hostert, P., Hughes, M. J., Huntington, J., Johnson, D. M., Kennedy, R., Kilic, A., Li, Z., Lymburner, L., McCorkel, J., Pahlevan, N., Scambos, T. A., Schaaf, C., Schott, J. R., Sheng, Y., Storey, J., Vermote, E., Vogelmann, J., White, J. C., Wynne, R. H., and Zhu, Z.: Current status of Landsat program, science, and applications, Remote Sens. Environ., 225, 127–147, https://doi.org/10.1016/j.rse.2019.02.015, 2019.
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., Huntington, J. L., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J.-F., Strobl, P., and Cook, B. D.: Fifty years of Landsat science and impacts, Remote Sens. Environ., 280, 113195, https://doi.org/10.1016/j.rse.2022.113195, 2022.
Xu, H.: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Int. J. Remote Sens., 27, 3025–3033, https://doi.org/10.1080/01431160600589179, 2006.
Yang, T., Zhang, G. L., Li, Y., Fan, J., Sun, D., Wang, J., Di, Y., You, N., Liu, R., Zhang, Q., and Doughty, R. B.: Satellite observed rapid green fodder expansion in northeastern Tibetan Plateau from 2010 to 2019, Int. J. Appl. Earth Obs. Geoinf., 102, 102394, https://doi.org/10.1016/j.jag.2021.102394, 2021.
Yang, T., Han, B., He, X., Ye, Z., Tang, Y., Lin, J., Cui, X., and Bi, J.: Diagnosis of the accuracy of land cover classification using bootstrap resampling, Int. J. Remote Sens., 45, 3897–3912, https://doi.org/10.1080/01431161.2024.2358546, 2024.
Yao, T., Bolch, T., Chen, D., Gao, J., Immerzeel, W., Piao, S., Su, F., Thompson, L., Wada, Y., Wang, L., Wang, T., Wu, G., Xu, B., Yang, W., Zhang, G., and Zhao, P.: The imbalance of the Asian water tower, Nat. Rev. Earth Environ., 3, 618–632, https://doi.org/10.1038/s43017-022-00299-4, 2022.
Zalite, K., Antropov, O., Praks, J., Voormansik, K., and Noorma, M.: Monitoring of agricultural grasslands with time series of X-band repeat-pass interferometric SAR, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 9, 3687–3697, https://doi.org/10.1109/jstars.2015.2478120, 2016.
Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., Joiner, J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y., Xiao, J., Asrar, G. R., and Chen, M.: Optical vegetation indices for monitoring terrestrial ecosystems globally, Nat. Rev. Earth Environ., 3, 477–493, https://doi.org/10.1038/s43017-022-00298-5, 2022.
Zha, Y., Gao, J., and Ni, S.: Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, Int. J. Remote Sens., 24, 583–594, https://doi.org/10.1080/01431160304987, 2003.
Zhang, C., Dong, J., and Ge, Q.: Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis, Comput. Electron. Agric., 197, 106946, https://doi.org/10.1016/j.compag.2022.106946, 2022a.
Zhang, H., Tang, Z., Wang, B., Kan, H., Sun, Y., Qin, Y., Meng, B., Li, M., Chen, J., Lv, Y., Zhang, J., Niu, S., and Yi, S.: A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data, Earth Syst. Sci. Data., 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, 2023.
Zhang, P., Jeong, J. H., Yoon, J. H., Kim, H., Wang, S., Linderholm, H. W., Fang, K., Wu, X., and Chen, D.: Abrupt shift to hotter and drier climate over inner East Asia beyond the tipping point, Science, 370, 1095–1099, https://doi.org/10.1126/science.abb3368, 2020.
Zhang, Y., Woodcock, C. E., Arévalo, P., Olofsson, P., Tang, X., Stanimirova, R., Bullock, E., Tarrio, K. R., Zhu, Z., and Friedl, M. A.: A Global Analysis of the Spatial and Temporal Variability of Usable Landsat Observations at the Pixel Scale, Front. Remote Sens, 3, 894618, https://doi.org/10.3389/frsen.2022.894618, 2022b.
Zhou, H., Wang, D., Guo, M., Yao, B., and Shang, Z.: Promoting Artificial Grasslands to Improve Carbon Sequestration and Livelihood of Herders, Carbon Management for Promoting Local Livelihood in the Hindu Kush Himalayan (HKH) Region, Springer, Cham, 211–228, https://doi.org/10.1007/978-3-030-20591-1_12, 2020.
Zhu, Q., Chen, H., Peng, C., Liu J., Piao, S., He, J.-S., Wang, S., Zhao, X., Zhang, J., Fang, X., Jin, J., Yang, Q.-E., Ren, L., and Wang, Y.: An early warning signal for grassland degradation on the Tibetan Plateau, Nat. Commun., 14, 6406, https://doi.org/10.1038/s41467-023-42099-4, 2023.
Zhu, Z. and Woodcock, C. E.: Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change, Remote Sens. Environ., 152, 217–234, https://doi.org/10.1016/j.rse.2014.06.012, 2014.
Short summary
The Tibetan Plateau is an important pastoral area where cultivated pastures play an increasingly important role. However, little is known about the spatial distribution of the cultivated pastures due to the difficulty in distinguishing them from natural grasslands with remote sensing. For the first time, we have mapped the cultivated pastures on the plateau at a resolution of 30 m with decent accuracy. This dataset is valuable to scientists, policymakers, conservationists, and pastoralists.
The Tibetan Plateau is an important pastoral area where cultivated pastures play an increasingly...
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